Overview

Dataset statistics

Number of variables12
Number of observations684
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory111.7 KiB
Average record size in memory167.3 B

Variable types

NUM6
UNSUPPORTED4
CAT2

Reproduction

Analysis started2020-07-26 14:52:10.980000
Analysis finished2020-07-26 14:52:29.564000
Duration18.58 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Id has unique values Unique
TotalBilirubin is an unsupported type, check if it needs cleaning or further analysis Unsupported
DirectBilirubin is an unsupported type, check if it needs cleaning or further analysis Unsupported
AlkphosAlkalinePhosphotase is an unsupported type, check if it needs cleaning or further analysis Unsupported
AlbuminGlobulinRatio is an unsupported type, check if it needs cleaning or further analysis Unsupported

Variables

Id
Real number (ℝ≥0)

UNIQUE

Distinct count684
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean342.5
Minimum1
Maximum684
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-07-26T20:22:29.822000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35.15
Q1171.75
median342.5
Q3513.25
95-th percentile649.85
Maximum684
Range683
Interquartile range (IQR)341.5

Descriptive statistics

Standard deviation197.5980769
Coefficient of variation (CV)0.5769286917
Kurtosis-1.2
Mean342.5
Median Absolute Deviation (MAD)171
Skewness0
Sum234270
Variance39045
2020-07-26T20:22:30.179000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
68410.1%
 
22510.1%
 
23310.1%
 
23210.1%
 
23110.1%
 
23010.1%
 
22910.1%
 
22810.1%
 
22710.1%
 
22610.1%
 
22410.1%
 
21410.1%
 
22310.1%
 
22210.1%
 
22110.1%
 
22010.1%
 
21910.1%
 
21810.1%
 
21710.1%
 
21610.1%
 
23410.1%
 
23510.1%
 
23610.1%
 
23710.1%
 
25410.1%
 
Other values (659)65996.3%
 
ValueCountFrequency (%) 
110.1%
 
210.1%
 
310.1%
 
410.1%
 
510.1%
 
610.1%
 
710.1%
 
810.1%
 
910.1%
 
1010.1%
 
ValueCountFrequency (%) 
68410.1%
 
68310.1%
 
68210.1%
 
68110.1%
 
68010.1%
 
67910.1%
 
67810.1%
 
67710.1%
 
67610.1%
 
67510.1%
 

Age
Real number (ℝ≥0)

Distinct count72
Unique (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.099415204678365
Minimum4
Maximum90
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-07-26T20:22:30.510000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile18
Q133
median45
Q358
95-th percentile72
Maximum90
Range86
Interquartile range (IQR)25

Descriptive statistics

Standard deviation16.30758699
Coefficient of variation (CV)0.3615919834
Kurtosis-0.5891489782
Mean45.0994152
Median Absolute Deviation (MAD)13
Skewness-0.03254672964
Sum30848
Variance265.9373935
2020-07-26T20:22:30.837000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
60416.0%
 
45273.9%
 
38263.8%
 
42243.5%
 
50243.5%
 
48223.2%
 
32223.2%
 
40213.1%
 
58202.9%
 
55202.9%
 
33182.6%
 
65182.6%
 
46182.6%
 
75172.5%
 
26152.2%
 
18142.0%
 
51131.9%
 
35131.9%
 
57131.9%
 
62131.9%
 
66131.9%
 
34121.8%
 
49121.8%
 
30111.6%
 
36111.6%
 
Other values (47)22633.0%
 
ValueCountFrequency (%) 
420.3%
 
610.1%
 
720.3%
 
810.1%
 
1010.1%
 
1110.1%
 
1220.3%
 
1350.7%
 
1430.4%
 
1510.1%
 
ValueCountFrequency (%) 
9010.1%
 
8520.3%
 
8420.3%
 
7810.1%
 
75172.5%
 
7450.7%
 
7320.3%
 
72101.5%
 
70101.5%
 
6920.3%
 

Gender
Categorical

Distinct count2
Unique (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
Male
518
Female
166
ValueCountFrequency (%) 
Male51875.7%
 
Female16624.3%
 
2020-07-26T20:22:31.212000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.485380117
Min length4

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e85027.7%
 
a68422.3%
 
l68422.3%
 
M51816.9%
 
F1665.4%
 
m1665.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter238477.7%
 
Uppercase Letter68422.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M51875.7%
 
F16624.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e85035.7%
 
a68428.7%
 
l68428.7%
 
m1667.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3068100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e85027.7%
 
a68422.3%
 
l68422.3%
 
M51816.9%
 
F1665.4%
 
m1665.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3068100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e85027.7%
 
a68422.3%
 
l68422.3%
 
M51816.9%
 
F1665.4%
 
m1665.4%
 

TotalBilirubin
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size13.4 KiB

DirectBilirubin
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size13.4 KiB

AlkphosAlkalinePhosphotase
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size12.2 KiB

SgptAlamineAminotransferase
Real number (ℝ≥0)

Distinct count152
Unique (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.59356725146199
Minimum10
Maximum2000
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-07-26T20:22:31.526000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15
Q123
median36
Q362
95-th percentile308
Maximum2000
Range1990
Interquartile range (IQR)39

Descriptive statistics

Standard deviation196.577834
Coefficient of variation (CV)2.270120521
Kurtosis43.21768505
Mean86.59356725
Median Absolute Deviation (MAD)16
Skewness6.115990795
Sum59230
Variance38642.84482
2020-07-26T20:22:31.777000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
25284.1%
 
20273.9%
 
22223.2%
 
21192.8%
 
28182.6%
 
18182.6%
 
48162.3%
 
15162.3%
 
31152.2%
 
30152.2%
 
24152.2%
 
27131.9%
 
33131.9%
 
32121.8%
 
29121.8%
 
36121.8%
 
26111.6%
 
14111.6%
 
42111.6%
 
12111.6%
 
35111.6%
 
37111.6%
 
16111.6%
 
38101.5%
 
40101.5%
 
Other values (127)31646.2%
 
ValueCountFrequency (%) 
1040.6%
 
1120.3%
 
12111.6%
 
1350.7%
 
14111.6%
 
15162.3%
 
16111.6%
 
17101.5%
 
18182.6%
 
1981.2%
 
ValueCountFrequency (%) 
200010.1%
 
168020.3%
 
163020.3%
 
135010.1%
 
125020.3%
 
95010.1%
 
87540.6%
 
79010.1%
 
77910.1%
 
62210.1%
 

SgotAspartateAminotransferase
Real number (ℝ≥0)

Distinct count177
Unique (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.1798245614035
Minimum10
Maximum4929
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-07-26T20:22:32.021000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15
Q125
median43
Q388.25
95-th percentile499.55
Maximum4929
Range4919
Interquartile range (IQR)63.25

Descriptive statistics

Standard deviation281.9611483
Coefficient of variation (CV)2.426937287
Kurtosis141.0984284
Mean116.1798246
Median Absolute Deviation (MAD)22
Skewness9.831824802
Sum79467
Variance79502.08914
2020-07-26T20:22:32.263000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
23202.9%
 
30162.3%
 
28162.3%
 
24152.2%
 
21152.2%
 
20152.2%
 
22142.0%
 
34142.0%
 
25131.9%
 
19131.9%
 
32121.8%
 
29121.8%
 
58121.8%
 
15121.8%
 
18111.6%
 
40111.6%
 
16111.6%
 
26111.6%
 
17101.5%
 
14101.5%
 
4391.3%
 
2791.3%
 
4291.3%
 
4191.3%
 
3191.3%
 
Other values (152)37655.0%
 
ValueCountFrequency (%) 
1010.1%
 
1130.4%
 
1271.0%
 
1330.4%
 
14101.5%
 
15121.8%
 
16111.6%
 
17101.5%
 
18111.6%
 
19131.9%
 
ValueCountFrequency (%) 
492910.1%
 
294610.1%
 
160010.1%
 
150010.1%
 
105020.3%
 
96020.3%
 
95020.3%
 
85071.0%
 
84410.1%
 
79420.3%
 

TotalProtiens
Real number (ℝ≥0)

Distinct count58
Unique (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.433187134502925
Minimum2.7
Maximum9.6
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-07-26T20:22:32.492000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile4.6
Q15.7
median6.5
Q37.2
95-th percentile8.085
Maximum9.6
Range6.9
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.081344976
Coefficient of variation (CV)0.1680885312
Kurtosis0.1222329834
Mean6.433187135
Median Absolute Deviation (MAD)0.7
Skewness-0.2768852305
Sum4400.3
Variance1.169306958
2020-07-26T20:22:32.736000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
7385.6%
 
6.8375.4%
 
6355.1%
 
6.2284.1%
 
6.9273.9%
 
7.2263.8%
 
7.1243.5%
 
7.3223.2%
 
6.4213.1%
 
5.5213.1%
 
8213.1%
 
5.6213.1%
 
5.8192.8%
 
6.1192.8%
 
6.6182.6%
 
7.5182.6%
 
6.7172.5%
 
6.5172.5%
 
6.3172.5%
 
5.9162.3%
 
5.2162.3%
 
5.4152.2%
 
7.4152.2%
 
7.9142.0%
 
5131.9%
 
Other values (33)14921.8%
 
ValueCountFrequency (%) 
2.710.1%
 
2.810.1%
 
310.1%
 
3.630.4%
 
3.720.3%
 
3.820.3%
 
3.930.4%
 
430.4%
 
4.120.3%
 
4.350.7%
 
ValueCountFrequency (%) 
9.610.1%
 
9.510.1%
 
9.220.3%
 
8.910.1%
 
8.710.1%
 
8.630.4%
 
8.550.7%
 
8.430.4%
 
8.330.4%
 
8.281.2%
 

Albumin
Real number (ℝ≥0)

Distinct count40
Unique (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1178362573099414
Minimum0.9
Maximum5.5
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2020-07-26T20:22:32.970000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.8
Q12.6
median3.1
Q33.7
95-th percentile4.3
Maximum5.5
Range4.6
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.7835258352
Coefficient of variation (CV)0.2513043568
Kurtosis-0.3794806307
Mean3.117836257
Median Absolute Deviation (MAD)0.6
Skewness-0.04155474107
Sum2132.6
Variance0.6139127345
2020-07-26T20:22:33.207000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3568.2%
 
4395.7%
 
2.9345.0%
 
3.2334.8%
 
3.1334.8%
 
3.9304.4%
 
2.7294.2%
 
2.5284.1%
 
3.3273.9%
 
2263.8%
 
3.5263.8%
 
3.4253.7%
 
2.6243.5%
 
3.6243.5%
 
3.7243.5%
 
2.8202.9%
 
2.4192.8%
 
4.1182.6%
 
3.8172.5%
 
2.1162.3%
 
2.3152.2%
 
4.3152.2%
 
1.8152.2%
 
2.2142.0%
 
4.2121.8%
 
Other values (15)659.5%
 
ValueCountFrequency (%) 
0.920.3%
 
110.1%
 
1.430.4%
 
1.540.6%
 
1.6111.6%
 
1.740.6%
 
1.8152.2%
 
1.981.2%
 
2263.8%
 
2.1162.3%
 
ValueCountFrequency (%) 
5.520.3%
 
510.1%
 
4.940.6%
 
4.820.3%
 
4.730.4%
 
4.640.6%
 
4.560.9%
 
4.4101.5%
 
4.3152.2%
 
4.2121.8%
 

AlbuminGlobulinRatio
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size13.2 KiB

Selector
Categorical

Distinct count2
Unique (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
1
493
2
191
ValueCountFrequency (%) 
149372.1%
 
219127.9%
 
2020-07-26T20:22:33.463000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
149372.1%
 
219127.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number684100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
149372.1%
 
219127.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common684100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
149372.1%
 
219127.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII684100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
149372.1%
 
219127.9%
 

Interactions

2020-07-26T20:22:14.890000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:15.389000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:15.962000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:16.343000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:16.731000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:17.117000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:17.482000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:17.841000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:18.191000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:18.557000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:18.915000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:19.278000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:19.627000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:20.017000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:20.388000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:20.789000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:21.172000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:21.552000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:21.931000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:22.304000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:22.667000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:23.057000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:23.437000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:23.803000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:24.163000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:24.537000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:24.902000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:25.286000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:25.659000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:26.077000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:26.426000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:26.794000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:27.149000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:27.677000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:28.038000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:28.391000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-07-26T20:22:33.705000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-07-26T20:22:34.001000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-07-26T20:22:34.301000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-07-26T20:22:34.606000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-07-26T20:22:35.023000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-07-26T20:22:28.962000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T20:22:29.440000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

IdAgeGenderTotalBilirubinDirectBilirubinAlkphosAlkalinePhosphotaseSgptAlamineAminotransferaseSgotAspartateAminotransferaseTotalProtiensAlbuminAlbuminGlobulinRatioSelector
0165Female0.70.118716186.83.30.91
1262Male10.95.5699641007.53.20.741
2362Male7.34.149060687.03.30.891
3458Male10.418214206.83.411
4572Male3.9219527597.32.40.41
5646Male1.80.720819147.64.41.31
6726Female0.90.215416127.03.511
7829Female0.90.320214116.73.61.11
8917Male0.90.320222197.44.11.22
91055Male0.70.229053586.83.411

Last rows

IdAgeGenderTotalBilirubinDirectBilirubinAlkphosAlkalinePhosphotaseSgptAlamineAminotransferaseSgotAspartateAminotransferaseTotalProtiensAlbuminAlbuminGlobulinRatioSelector
67467532Male3.71.661250886.21.90.41
67567632Male12.1651548926.62.40.51
67667732Male2513.756041887.92.52.51
67767832Male158.228958805.32.20.71
67867932Male12.78.419028475.42.60.91
67968060Male0.50.150020345.91.60.372
68068140Male0.60.19835316.03.21.11
68168252Male0.80.224548496.43.211
68268331Male1.30.518429326.83.411
68368438Male10.321621247.34.41.52